{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import time\n",
    "\n",
    "from matplotlib import rc\n",
    "\n",
    "rc('text', usetex=True)\n",
    "rc('font',**{'family':'serif','serif':['Computer Modern Roman']})\n",
    "\n",
    "%matplotlib inline\n",
    "palette = sns.color_palette()\n",
    "\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data(num_samples=5000, offset=0, alpha=2, beta=4):\n",
    "    dist = torch.distributions.beta.Beta(alpha, beta)\n",
    "    data = []\n",
    "    for i in range(num_samples):\n",
    "        data.append(dist.sample().item())\n",
    "    return torch.tensor(data, requires_grad=False, device=device) + offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0039) tensor(0.9310)\n",
      "tensor(0.0870) tensor(0.9931)\n"
     ]
    },
    {
     "data": {
      "image/png": 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2vwRlesuO8+mib0eq6hpYuT+XaYO64eFqUh1HOSEEd43pwaG8CjYeLlQdx/oKD8Oy3xtbEIz5jeo0tu3yP4K5Hrbpw/nOp4u+HVmxL5equkauGRKpOorNmDG4G8G+HizclKU6inU11BpbDrh6wswF4KJ/6F9SYA/oOx12LNR9++fRRd9OSClZuCmLuBAfkrs7z347LfFwNXHbyFg2HCrgSH656jjWs/qvxnTEa1+HLno8p1VGPWQMev+od+M8ly76dmJL5hn2Z5dx95g4XFz0bI1z3ZISi4erCws3HVMdxToOrYTU12DEffZ3qLlK0ckQNhD2fKI6iU3RRd9OvLUxiyAfd65L1F075wts+ndZuusURZV1quNYVnku/Pd+o3hN+pvqNPZn4PVwajsUH1OdxGboom8HjuSXszY9n1tHxuLppvtyL+TO0T2obTDzYepx1VEsx2w2zrmtrzZWm7o53z5LHdb/OuN231K1OWyILvp2YOGmLDxcXbg1xcmX2l9CfJgf43qH8H7qcWobHGSx1pZXjAPOr3oaQnqrTmOfusZCVLIu+ufQRd/GFVbU8vmu01yXGEWQr4fqODbtrjE9KCiv5ZsfHWBudv5BWPsPYwbK0FtVp7FvCdMhby+UZatOYhN00bdxH2w5Tl2DmbvG9FAdxeaNiw8mPtTX/hdrNdbDF/cZ2wVPf0Fvs9BRPScYt/qELUAXfZtWU9/IB6nHmdg3lF6hvqrj2LzmxVoHcspIzSxSHaf9Nv0HcnYb++r46pXXHRbaH3xC4eha1Ulsgi76NmzprtMUVdZxz7g41VHsxrVDIwn0ceedzXa6WCtnD2x4GgbMgn7XqE7jGFxcoOflxviI2aw6jXK66NsoKSXvbM5iQGQXRvTQi7Fay9PNxOykaNak55NXZmd77TfUGtMzvYNg6rOq0ziWnhOgqhBy96hOopwu+jYqNbOIw/kV3DayO0L36bbJnOHRNJoln2w/qTpK26x/CvL2wYyXjPNfNcvpMc64Pf6D2hw2QBd9G7Uo9TgB3m7MGByhOord6R7sw5hewXyy/SSNZjsZ0D261ujLHzpXr7q1hi4R4B8DJ7eqTqKcLvo2KL+shpX7c5mdFK0XY7XTTckxnC6p5vtDBaqjtKwsBz6/B0L6wlW6W8dqopONom/PM7ssQBd9G/Tf3adpMEvmDI9WHcVuTe4XRrCvO4u32fhxio0N8PndUF8FN7wL7t6qEzmu6BHG4Sqlp1QnUUoXfRsjpeTznacZGhNAXIieptle7q4u3JAUzdr0fHJLbXhAd8PTcHwTTHveONxbs57mQ2ecvItHF30bcyCnjIy8cq5LjFIdxe41D+h+vN1GW/tH18L3z8KQuTDkJtVpHF/YAHDzhpPbVCdRShd9G/PFrtO4mQQzBuk90zsqNsiHy/qEsCj1hO3tx1NRAEvnGf34enpm5zC5QuQwY9dNJ6aLvg2RUrJ8Xy7j4kMI8HZXHcch3DWmB4UVtXy124b2XZESvv4V1JQZu2fqfvzO020w5B8wxlKclFWKvhBikhBi1SUeLxZC7BRCPG2N69urvadLOV1SzZUDwlVHcRhjegXTO8zG9uPZ8ylkLINJf4GwfqrTOJdug6GhBgoPqU6ijFWKvpRydQtPuUFKOUxK+ag1rm+vlu/LxeQimJQQpjqKwxBCcPfYONJzy1mfYQPTN2vLYdUTRjfDiPtVp3E+4YOM25wf1eZQSFX3ToAQQm8ocw4pJSv25TIyLoiuPrprx5JmDo0kMsCLl9YeVt/a3/g8VOTCVc8Ye8JonSs4Hly9nHo7BlX/6wKBIiHE/As9KISYJ4TYIYTYUVBgA62zTnAor4Kswkqu0F07FudmcuH+y3qSdqKETUcK1QWpLIStb8DAGyAqSV0OZ+ZigvABuqXf2aSUC6SUJUCJEGLWRR5PklImhYQ4x9ayK/blIgRc0U937VjDDUlRRAZ48ezKDMyqtmZIfd04+nDc79VcXzN0Gwy5e512x81OL/pNrfjmQn+ms69vq5bvy2FYTFdCu+hzUK3Bw9XEbyf3Zs+pUr7Zq+BkrZoy2PYmJMyAkD6df33tJ+GDoLYMiu10++0OstbsnVlA0rmt+HNm83yK0cKfBCClXGKNDPbkWGEl6bnletaOlV07NJKEbl14ZkU61XWdPG9/76dQWwqjH+7c62o/122wceuk/frWmr2zRErZ9dyCLqWc3HRbIqVc3fTxjDWub29W7M8F4Ir+uuhbk8lF8MT0fpwqrua19Uc69+Jpi4wVoZGJnXtd7edCE8DF1Tiwxgnp6QM2YOX+XAZEdiE6UC/SsbaRPYOYOTSSNzYc5Uh+RedcNHcfZKcZB5zrsxHUc/WAkASnHczVRV+x3NIa0k6UcKVu5XeaP05NwMfDlUc++5GGxk4YzEtbBCZ3GDTb+tfSWqfbYKPoq57Cq4Au+oqtOmB07ej+/M4T4ufBP64ZwI8nS3h9/VHrXqyhFvZ8DH2n6dOwbEm3QcbxieUKBvUV00VfsRX7c4kL8aFXqJ/qKE5lxuAIZgyO4IU1h9maacVJZBnLoLrYOBFLsx3Ng7lO2K+vi75CJVV1pGYW6a4dRf41cwAxgd489FGa9Q5RT1sEXaIg7nLrvL7WPmEDAOGU/fq66Cu0+mA+jWapZ+0o4ufpxutzE6msbeDOd7dTWWvhnRdLT8GRNTDkZmMlqGY7PHwhqKdTTtvURV+hlftz6ebvyaAof9VRnFbf8C68cnMiB3PKuPeDnZadv7/7I0DC0Fss95qa5YQPhLx9qlN0Ol30Famqa+D7QwVc0T8coafxKXV531CenTWYH44Wcts726iwRIvfbIa0D6DHOOjaveOvp1le2AAoPgY1paqTdCpd9BVZl15AbYNZd+3YiOuHRfHinKHsPF7M3Le2UlpV37EXPL4JSo4bc/M129S8zXLefrU5Opku+op8kXaaUD8PknvoaXy2YsbgCF67JZH92aXMeuMHThZVtf/F0haBh7+x145mm8IHGre5ztXFo4u+Amcqalmfkc/MoZGYXHTXji25on84792ZTF5ZDTNf20zaieK2v0h1CRz4EgbOAjcvy4fULMMvHLyDnG4wVxd9Bb7+MZsGs+S6xCjVUbQLGNUzmKW/HI23uytzFqSyrK27cu773DiSL1F37dg0IYzWfu5e1Uk6lS76nUxKyac7TtE/ogt9wvWCLFvVK9SXL345iv4RXfjlh7tY8H0bVu7uet8YJOw2xHoBNcsIGwD5B53qoHRd9DvZ1qwiDuSUMTclVnUUrQVBvh4svieFaYO68a9l6by4+nDLn3RqJ+TshmG3683V7EH4IGishTOt+No6CFfVAZzNwk1ZBPq4M3NopOooWit4upl4ac5QvNxM/Gf1ITzdXLh3fM+Lf8L2N8HdFwbd2HkhtfY7O5i719hy2Qm0qqUvhHjd2kGcwdGCClYfzOOWETF4uukVmvbC5CJ45vpBTBvUjSeXp/PVj9kXfmJlIexbCoNvAs8unRtSa5/geGMHVCfq129t984SIcREIcQEIYT+39xOTy47iLebiV+M7K46itZGLi6C524YzPDuXXl0yZ4L78Wf+jo01kHyPZ0fUGsfk5vRwtdF/2e2N30MA9Y0FX89StUGGw4VsPpgPg9NjCfEz0N1HK0dPN1MvHJzIl7uJh5cvIvahnO2bKgqgq3zod81+gxce9M8g8dJ9tZvbdFfC7wJ7JRSDpdSrpVS7hZCTLBiNodR32jmH98coHuQN3eM7q46jtYBYV08eXbWINJzy3nz+8yfHtjyKtSVw/g/qAuntU/YQGNv/Yo81Uk6RWuL/pNSyhullGuFEEOEEP5CiKesmsyBLEo9zpH8Ch6f1g8PV92Xb+8mJoRx1YBwXll3hFPFVVCQAT+8BANmQVh/1fG0tmoezHWSvfVbW/TPLkuUUu4GJkopH5NSrrVOLMdRVFnHf1YdYmx8MBMTQlXH0Szk8en9EAj+veIAfPUrcPOGK59UHUtrj/ABxq2T7K1/ySmbQojrgclAkhDiKCAACWQCS60fz/49vyqDyrpGnpjeT++m6UAiA7z4xcgYYrc8DqZUmDkffPUPdbvk6Q9BvSB7l+okneKSRV9K+bkQYjUQJ6VM66RMDuNgThmLt57gFyO7Ex+mV986lIZaHq5bgJdpDWuCbmHi4DmqE2kdEZEIWd+rTtEpWuzekVKWnl/whRDdrRXIkTz3XQZdvNx4eFK86iiaJZ05Cgsn47X7HX4Iu5l5OdPILqlWnUrriMhEqMiFsouswXAglyz6QohPmm6/E0J80vTxKbCqU9LZsUN55aw+mM/to7oT4O2uOo5mKXuXwPzxUHwcbvqY6Bufwyzho20nVCfTOiIi0bg97fhdPC1tw/BY0+2j57b2hRBDrRfJMczfkImXm4nb9EIsx2BuhBWPwbYFED0Crl8IAdFEAxP7hvLRtpM8NCEed1e9nZVdCh8IwmT06ydMV53Gqi75P1RKmdX0x65CiO5N0zV/xzmzebSfK6qs46sfTzM7KYquPrqVb/fMjfDFfUbBT3kAbv8WAqLPPjw3JZbCilpW7M9VGFLrEHdvCO3nFC39VjdLpJTHgM8wFmklWiuQI1i+L4f6Rsns4dEtP1mzfauegL2fwoQ/w5X/Mpbun2NcfAixQd58sOWYkniahUQmQnaaw6/MbW3RF02rbz+XUjrXKcLt8PWP2cSF+NCvm96myO6lfQhbXoHke2Hc7y74FBcXwdwRsWw/VszBnLJODqhZTGQi1JRAUWbLz7VjrS36RcAU4MmmufvDrRfJvuWV1bA1q4irB0foefn27sxRWPY76D4WrvjXJZ96Q1IUHq4ufJB6vJPCaRbXPJib7diz01tV9KWUaU0rcEullJ8DeunhRSzfm4OUMH1QhOooWkeYG2HpPKMrZ+Z8MF16zkOAtztXD47gv2mnKaup76SQmkWFJoCrp8P367d2P/0hQognmz6ewujb1y5g4+FCegT70CvUV3UUrSN2vA2nd8DU58C/dQfe3Doylqq6RpbuPGXlcJpVmNyMk7QcfGVua7t3JgMLmj7mA0uslsiO1TeaSc08w+heQaqjaB1Rngdr/gFxl8HAWa3+tEFRAQyODuCD1ONIBx8MdFiRicYePA58Zm5ri/5OKWVW8wd6cdYF7TlVQmVdI2N6BauOonXEqj9DQ7XRym/juMytKbEcLahk85EzVgqnWVVEItRXQUG66iRW09qi/5gQYrtekXtpmw6fQQhIidMtfbuV9T3s+QRG/xqCe7X506cP6kaonwcvrTmsW/v2KLppjsrJrWpzWFFri/7TTYen3CilnA3MtmYoe7X5SCEDI/31tgv2qqEOvn0EAmJh7CPteglPNxMPTujFtmNFbDpSaOGAmtV17QG+YXAiVXUSq2lt0Y8TQrzefIAK0NWaoexRbUMju0+WMFK38u3Xlpeh8BBM/Te4ebX7ZW4cHk2EvydPLU+nrsFswYCa1QkBMSm66ANHpZT3A6I1i7OEEJOEEBftAhJCzGp6jsOcLXcgu4y6RjNDYwJUR9Hao/g4bHgWEmZA7ykdeikPVxNPzOjP/uwy/v1dhoUCap0mZiSUnoBSx5yF1dqiP6zpIPSuTStzL7kNg5Ry9cUeE0LMOuc5JUKISa0Na8t2nywBYEi0/iXILq14DIQLXGmZU0CvHBDO3JQYFnyfyavrjmA26/59uxGTYtw6aGu/tUV/ATAHY9fNRCnlvztwzeEYJ2/RdOsQ+/iknSihm78n4f6eqqNobZW+DDKWwWWPgn+UxV728Wn9mD6oG8+uzGDGK5uYv+EoaSeKaWjUXT42LWwguPvCiS2qk1hFS1srA8ZBKvy0zXJHnd//8bNOcCHEPGAeQExMjIUua127T5YwJFp37did2gpY9nsISYCUX1r0pT3dTLx801DGxYfwfuoxnlxuTAMM6+LBL0Z2595xcbia9FbMNsfkarT2HfQkrRb/xwkh7hFCHBFCNAohDgsh7u7gNUuAwEs9QUq5QEqZJKVMCgkJ6eDlrO9MRS0niqp0f749WvdPKDsNV7/0s90zLUEIwezh0Xzz0FhS/28ir9w8lL7hXXh2ZQZ3vLud0mq9ZYNNirvMGNQvPWubV7UAABwuSURBVK06icW1dHLWPU1/HCalNAFJxt0dKvzb+am1H4cDzPnX/fl26vRO2PoGJN0J0clWv1y4vyfTB0Xw3p3JPHP9IFIzz/Dg4l006v5+2xN3mXGbtUFlCqtozRm5bzbP2GnacO1N4JLLFJsGa5OaB22b7lvV9BpLMKaATmr6+0UHfe3FvtNlCAH9I/RWynajsR6++jX4hMKkv3T65WcPj+ZvVw9g4+FCXl57uNOvr7UgtD94B0PmetVJLK6lPv2LrSU/eqlPairsS867b/I5f36mVensxP7sUnoE+eDj0aohEs0WpL4GeXth9gfg6a8kwk3J0WzLOsPLa48wdWA3eof5KcmhXYCLC8SNN4q+lG3ejsOWtdTSj2takPU/HzjIjBtLOZBTRj/dyrcfxcdg3ZPQZ6oxL18RIQRPzOiPr4crf/t6v962wdbEXQYVeQ63D09LTdM5GFMsz/8x1wPoyLRNh1FaVc+p4mpuHmEfs4ycnpTwzW/BxQRTn1Xeggv0ceeRKb154sv9rDqQx5T+4UrzaOeIu9y4zVxv7LXvIFoq+vdIKX92jIwQYqiV8tidA03H4/WPUNNFoLXRnk/g6Bq46hmLzsnviJuTY3j3h2M8990hJiWE4eLiOF0Jdi0gGgJ7GkU/5X7VaSzmkt07Fyr4l7rfGe3PNnal0Ofh2oGKAmPlbVQyDO/ozGPLcTW58JtJvcnIK+ebvTmq42jnirsMjm0yBv4dhF4Z0kEHcsoI9fMgxM9DdRStJSseMxZjXf2S0b1jQ6YN7EbfcD9eWHVIr9i1JXGXQV2FMb3XQeii30EHsvUgrl04tBL2LYFxv7PJ/lkXF8FvJ/cms7CSL9Icb0GQ3eox1tiT6Yjdzyw/Sxf9DqhvNHO0oII+4XqqnU2rLTcGb0P6wpjfqE5zUZP7hTEoyp8X1xzWWzLbCq+uEJ0CGctVJ7EYXfQ7IKuwkvpGSV9d9G3b6r81bbXwMrjabjecEIJHpvThVHE1n+44qTqO1qzvVMjbZ2y/7QB00e+A9NxyAPqE6e4dm3V6F2x/C5LndcpWCx01Lj6Y4d278vLaw9TUN6qOo4GxngMcprWvi34HZOSWYXIR9Az1UR1FuxApYeUfwScYJjyuOk2rNLf288pqWZTqGC1LuxfUE4L7GNtvOwBd9DsgI7ecuGAfPFxtayaI1uTgV8ae6Jf/CTzt57exlLggxvQK5vX1R6msbVAdRwOji+f4ZqguUZ2kw3TR74CMvHI9iGurzI2w9p/G4G3iL1SnabPfTunNmco63v3hmOooGhhdPOYGh5jFo4t+O1XUNnCyqFoP4tqq/V9AYQaMf9Tm5uS3RmJMVyb2DWX+hqN6z31bEJlk7Mia/q3qJB2mi347HcozBnH1zog2yGyGDc9AaD/od63qNO32m8m9KatpYOGmLNVRNBcX6HOl0dJvqFOdpkN00W+njKaZO33D7aev2GkcWm608sc+Ynyz2qkBkf5MHRjO25uyKKq070LjEPpMhdoyOLZRdZIOsd/vCMUycsvxdjcR1dVLdRTtfD+8DAExdt3Kb/abSb2prGvglbVHVEfR4i4DNx9I/0Z1kg7RRb+d0nPL6B3mp3dEtDUntxszdlIeMA64tnPxYX7MGR7De1uOcbBpR1dNETcviJ8MB78xJgrYKV3020FKSUZuuR7EtUU/vAieATB0ruokFvPolX0I8HLjT1/s1efpqpYwAyrz4eQ21UnaTRf9diioqKW4ql5P17Q1Z44arbDhd4GHr+o0FhPg7c6fp/dj14kS3c2jWvwUMLnDwa9VJ2k3XfTbIePs9gu66NuULa+AyQ2S71WdxOKuGRLBzKGRvLjmEJuPFKqO47w8uxgnah382ljxbYd00W+Hs0Vft/RtR0UB7F4Mg+eAX5jqNBYnhOAf1w6gZ4gv897fwY8n7X9lqN1KmAGlJyDnR9VJ2kUX/XZIzy0n2NeDIF/b3bHR6Wx/ExpqYORDqpNYja+HK4vuHkGgrztz39rKuox81ZGcU5+pxh77dtrFo4t+O+hBXBtTVwXb3jS+GUN6q05jVWFdPPl43kiiA725893t/EeftNX5fIIgdrQu+s6i0Sw5pPfcsS2pr0J1EYx+WHWSThEZ4MWS+0cyc0gkL645zJwFqZwqrlIdy7kkzDAWABba38C6LvptdKKoitoGsy76tqKiADa9CH2nQ8wI1Wk6jbe7K8/fOIQXbhxCem45V724kTUH81THch69rzRuD69Um6MddNFvo4xcY4GM7t6xEWv+CvVVMOmvioOoce3QSJb9aiwxgd7c/f4OXlpzGLOey299XWONHVwP6aLv8NJzyxEC4kN10VcufRmkLYJRD0FwvOo0ysQEefP5/aO4dkgkz686xH2LdlKh9+G3vvjJcPwH4wxmO6KLfhtl5JYTG+iNl7v9bdfrUIoy4auHIGwgXP5H1WmU83Qz8fzswfx5ej/WpOdz7aubz+4Eq1lJ/BVgrofM9aqTtIku+m2UkasHcZUry4EPZoI0ww3v2PRh551JCMFdY3rwwZ3JFFXWMe2ljTyzIp3qOvvdJ8amxaSAh7/ddfHoot8GNfWNHDtTSR+9nbI6uXvhrYlQWQi3LHHqbp2LGdUrmO9+M44ZgyN4bf1RJv9ngx7ktQaTG/S8HA6vsqvVubrot8HhvArMUg/iKiGlMRf/rUnGn+9YBlHDVKeyWcG+Hjw/ewgfz0vB083EXe/t4IHFu6iq0339FhU/BSpyIXeP6iStpot+GzRvbZvQTbf0O1VFPiy+EZb9DrqPgXnrodtg1ansQkpcEMt+NZbfTenN8r05zFmQqg9ksaT4ycbtoe/U5mgDXfTb4GBuGV5uJmICvVVHcR6HvoPXRxmDZVc9Y3TpOODeOtbk7urCgxPiWXBrEum55Tzw4S69itdSfEMhItGu5uvrot8GB3PK6BPuh0kfnGJ9jfXw3eOw+AbwDTNa9yPuBaH/7dtrUr8wnpw5kC2ZZ3h6RbrqOI4jfjKc3gk1paqTtIou+q0kpSQ9t1x37XSGsmx4b4Zx7GHSXXD3GgjrpzqVQ7h+WBQ3j4hh4aYs9p22jyJl87qPNWaSHf9BdZJW0UW/lXLLaiipqiehmx7EtaqcPTB/vHF73Vsw/Xlw81SdyqE8emVfAn3cefy/+/TqXUuIGg4mDzi2SXWSVtFFv5XSc4yFLrqlb0XHNsG704ypcPesgUE3qE7kkPy93HjsqgR2nyzh2705quPYPzdPiE6GrO9VJ2kVqxR9IcQsIcQkIcQfLvJ4sRBipxDiaWtc3xoONM3c0QuzrCR9GXxwHfh1g7u+g9AE1Ykc2syhkfQK9eXltXqvHovoPtZYQ1JVpDpJiyxe9IUQswCklKuBEiHEpAs87QYp5TAp5aOWvr61pOeWE9XViy6ebqqjOJ6s7+Gz2yB8ANy5AvyjVCdyeCYXwUMTenEor4IV+3NVx7F/3ccAEk5sUZ2kRdZo6Q8HMpv+nAkkXuA5AUKIOCtc22oO5pTRV6/EtbzcvfDxLRAYZ0zH9A5UnchpTB8UQVyID6+vP4q0oxWlNikqCVw9IWuj6iQtskbRDzjv70EXeE4gUCSEmH+hFxBCzBNC7BBC7CgoKLB4wLaqqW8ks6CCfnoQ17KKj8Oi68HDD+Z+rgt+JzO5CO4eE8fe06Vsy7L9bgmb5uoB0SPsYjDXGkW/BKOoX5SUcoGUsgSj+2fWRR5PklImhYSEWCFi25zdfkEP4lpOZSEsus4413bu57pLR5HrEiPp6u3GW5uyVEexf93HQp7t9+tbo+hv56fWfhyw6twHm1rxzYX+jBWub3EHc/X2CxZVVwmLZ0PpKbj5Uz1oq5Cnm4lbU2JZfTCPrMJK1XHsW4+xxq2Nt/YtXvSllEuAuOYB3KYBXYQQzcX/U84Z4G16vk07mKO3X7CYxnr49DbIToNZ7xjb02pKzR0Zi5uLC2/r1n7HRCSCm7fNF31Xa7yolPKZC9w3uem2BFjddPfq859ni9JzyvX2C5ZgboQv7oUjq2DGS9B3qupEGhDq58k1QyL4bOdJHpnSmwBvd9WR7JOru1306+vFWS2QUnIwt0yvxO0osxm+fBD2fQ6T/gbDblOdSDvH3WPjqKk38+HWE6qj2LfYUZB/AKqLVSe5KF30W5Bdamy/0E/357eflLDsEfhxMVz2fzDmYdWJtPP0CfdjbHww7/1wjLoGvQNnu8WMBCSc3KY6yUXpot+CvaeMTakGRPorTmKnGmph6T2w420Y/TCMt5v1eE7n7rFx5JfX8vWP2aqj2K/IYeDiZtOLtHTRb8G+06WYXISeudMexcfhnamw9zOY+BeY9Fe9NbINGxcfTHyoL29uzNSLtdrL3RsihsBxXfTt1r7sUuJDffF0M6mOYj+khJ3vwhtjoPAQzH4fxv5WF3wbJ4TgvvE9Sc8tZ/k+vTVDu8WkQPYuqK9RneSCdNG/BCkl+06X6q6dtshOg4VT4OtfG0ca3rcR+l2jOpXWStc2bcT23HcZ+nSt9ooZBY11RuG3QbroX0JuWQ2FFXUM1EW/ZeV58NVDsOByKM6Ca16F276Grt1VJ9PawOQi+N2U3hwtqOSTHSdVx7FPzWtPbPRQFavM03cUPw3i6v78i6ophc0vQeprRutm5AMw/g/gqX9Q2qsr+oczokcgTy1PZ3JCGKFd9CE2beIdCCF94USq6iQXpFv6l7AvuwwXAf266QL2M/U18MMr8OIQ2Phv6H0lPLANrvinLvh2TgjBk9cNpLbBzJ+/3KcHddsjJgVObjUWJNoYXfQvYd/pUnqF+uLlrgdxz5ISDn4NrybDd38y+u3nrYcb3oGgnqrTaRYSF+LL76b0ZuX+PN7aqLdnaLOYUVBbZizUsjG6e+cS9p4uZWyvYNUxbEfxMaPfPut7CEmAuUuh10TVqTQruWdsHGknSnhy+UFigry5on+46kj2I3akcXt8C4QPVJvlPLqlfxF5ZTUUlNfqmTvNjqwxDizP/hGm/hvu26QLvoMTQvDsDYMZFBXAAx/uYsU+fZ5uq/lHQ5dIOGF7g7m66F/EvtPGIO7AKF30yVgOi2809ry/dz0k3wMm/UuiM/D1cOWDu5IZFOXPA4vT+HaPLvytIoSxJcOJVKNL1Iboon8Re0+XIgR6z51jm+CTW41fUW//1jjWUHMqfp5uvH/XCBJjAvjVx2l8ul1P5WyVmBQozzG6RW2ILvoXse90KXHBPvh4OHGLtijLKPhdu8OtS8Hr/JMwNWfh6+HKu3ckM6pnEH/4fA+vrD2sZ/W0JHaUcWtjUzd10b8AKSV7TpU696Kshjr47HaQZrj5E/DqqjqRppiPhysLbxvOzKGR/Pu7Qzzx5X4azbrwX1RIgjF92cb69Z24GXtxp0uqyS+vJTHWiQvd2n9Azm648UM9FVM7y93VheduGExoFw/mb8ikoLyWF+YM0XtTXYiLC0Sn2Nzma7qlfwE7jxsHICTGOGnRP7oOfngJku6EhOmq02g2xsVF8H9XJfDn6f1YsT+XX7y9jdLqetWxbFNMCpw5DJWFqpOcpYv+Bew6Xoy3u4m+4U54WlZlIXxxHwT3gSn/VJ1Gs2F3jenBSzcNJe1EMbPf2EJOabXqSLbnbL++7bT2ddG/gB3HixkSHYCrycn+eaSE//4Sqotg1kJjb3BNu4SrB0fw3h3JnC6p5vrXfji7X5XWJGIomDxsajDXyapayyprGziYU0aSM/bnp74Oh1fClP9nc6sINds1qlcwn9ybglnCzNc28++VGVTUNqiOZRtcPYzTtGxox01d9M+z+2QJZonzDeJmp8GqJ6DPVEiepzqNZmf6R/iz8uFxXD04glfWHWH8M+t4YfUhskt0lw+xIyHnR6irVJ0E0EX/Z7YcPYPJRThX0a8pgyV3gm+osQ++PuFKawd/bzeev3EI/31gNAOj/Hlh9WFGPbWWGS9v4uU1hzmYU+acc/tjRoJshFPbVScB9JTNn9l0pJDBUf508XRTHaVzNDYYBb/4uHHoiXeg6kSanRsSHcC7dyRz/Ewly/bm8t2BXJ5bdYjnVh2im78nl/cNZUKfUEb1CsLb3QlKUPQIECbIXA9xlykOo4v+/yitrmfPqRIevLyX6iidQ0pY/gc4sgqm/we6j1adSHMgsUE+3H9ZT+6/rCf5ZTWsy8hnbXo+X6adZvHWE3i7m7hqQDduSIoiuXsgLi4O+humZxdj6ubh1TDpr6rT6KJ/rtTMM5gljHaG7ZSlhO8ehx0LYfSvjTn5mmYloV08uXF4DDcOj6G2oZHtWcV8syebb/bk8PmuU/QK9eXhSfFMHdDNMYt//GRY/Vcoy4YuEUqj6D79c2w+Uoi3u4mhjr4oq7HB2Bd/yysw/B6Y9DfViTQn4uFqYkx8ME9dP4htf5rIczcMRgAPLk5j6ksb+f5QgeqIlhc/xbg9slptDnTRP0tKydr0fFLignB3deB/lvI8+OBaSPsAxv0Bpj6rB241ZbzdXbl+WBQrHh7Hi3OGUFPfyC/e3sb/Ld3rWNM+Q/uBXwQc/k51El30m+05Vcqp4mquHOCgpwNJCXs+g9dHwakdMHM+TPiTLviaTTC5CK4ZEsmKh8dx7/g4Pt5+gitf+J7tx4pUR7MMIaDPVcZhRLUVSqPoot9k2d4cXF0EU/qFqY5ieUVZ8OEsWHo3dI2Feetg8BzVqTTtZzzdTPzfVQl8du9ITC6COQtSmb/hqGNM9Rw4C+qrIP1bpTF00cfo2vlmTw5j4oMJ8HZXHcdyKgth+aPwynBjGfhVz8BdqyA0QXUyTbukpO6BfPPQGK7oH8aTy9O55/0dlFbZ+aZu0SnGMYp7P1MaQxd9jF01T5dUM3VgN9VRLKOiANb8HV4cAtsWwJCb4MHtMOJecNFb4Gr2wc/TjVdvTuQvM/qx4VAB017eyJ5TJapjtZ+Li9HaP7rW+B5VFUPZlW3IWxuz6OLpyjR7L/oFGfDtI/DCANj4PPSaAL/cCle/rHyamKa1hxCCO0b34NN7RyIlzHp9Cx9sOWa/3T2DbzJW5+5YqCyC0xf942cqWXkgl1tSYu3zaMSaUti9GN6+El5Nhp3vwcAbjJb97PchpLfqhJrWYUNjuvLNQ2MY3SuIP3+5n7kLt3Ior1x1rLYL6WPsb7X1DWV78dhhlbOsNzZk4uoiuH1UdzUBpDQOTj5zBKpLjF35fEMhIBZ8w4xfCc/V2AAFB419PDKWG0u7G+uMA8sn/x0G3wy+ISreiaZZVVcfdxbeNpwPtx7n2ZUZXPHC91zZP5xZw6IYEx+Mh6uddF2O+Q0sXAY73oFRD3b65Z266G8/VsRH205w+6juhHXx7NyLVxYaX/Rd70HpyQs/x+RuDPz4hhl98TUlUHgEGpp2LvSPMXbETLgaopP19EvN4bm4CG4d2Z1pgyJ4e1MWH6QeZ/m+XPw8XZmUEMbInkGk9AgiOtALYavfD9HJEHc5rH8K+l0DAdGdenlh631jSUlJcseOHRZ/3dKqema+tpm6RjMrHx7XeV07OXtg63xjBL+x1vjiJ0yHsAHgFQgNNVCRByXHjU3QSk4YPyBko3HIctfuEJkEkYlG695W/2NrWieoazCz+Ugh3+7NYc3BPIqbZvh08/ckuUcgI3oEMSIukLhgH9v6IVB8DF4fbey1P3cpmCxff4QQO6WUST+73xpFXwgxCygBEqWUz7T18XNZo+gXVtTyi4XbOJJfwbt3DmdUTyvvtVNXCYdWwPaFcHwzuHkbAzoj7jX6+DRN6zCzWXKkoIKtmWdIzSpia2YRhRW1AAT7ejCiRyAj4gJJig0kNshb/Rjerg/gqwehzzSY9Ta4Wba3odOKflNBR0q5RAgxD8iUUq5u7ePns1TRl1JyoqiKb/bkMH/DUWobzMy/dRiX9Qnt8Gv/TF2V0WWTnQaHVxl97/WVTd0x90DireDl4Pv7aJpiUkqyCivZmlXE1swzbM0qIqe05uzj/l5uhPh5YBLi7C/MjWaJWUqkBAkEeLsR7OtBqJ8HEQFeRHX1IqqrN1FdvQjx9ej45nBbF8Dy3xtjeON+b6za9bFMI/RiRd8aP+qGA580/TkTSARWt+Fxi3h6RTp7T5VSWddAVW0j2aXVlNcYe3mM6x3C49MS6B3WzoPPD3wFuz80BlAb6oxumoZa4++VhVBV+NNzvQJh0GwYcL1xSLKeJ69pnUIIQVyIL3EhvtyUHIOUklPF1aSdLOF0cTXZJdUUVtRibmr4SmlsB+HiInARAiklxVV1nDhTxY5jRWe7jpq5m1wI8fPA292El7sJLzcTnm4mXF0E/SP9+e3kVsycGzEPguONHW+/ahrU9QwAv27gFw7Xvmbx6dbWKPoB5/09qI2P0/QbwDyAmJiYdoUora6nqq4BXw9XQnw9SO4RSHyYL+N7hxAb5NOu1zyrttzYItXVwzj02N0XvIPA5GYcmBAQbbTqwwdASF9d6DXNBgghiA70JjrQu12fX1nbQHZJNaeKqzlVXMWpkmoKymqprm80PuoaKamqo8Es6RbQhq6anpfDvRshZzdkfQ9lp6E8B8pzwdXyE0ysUfRLgEsdv9TS40gpFwALwOjeaU+If8204sHeQ28xPjRNcxo+Hq7Eh/kR394egktxcTEmZ0QmWv61z7+UFV5zOz+15uOAVW18XNM0TbMSixd9KeUSIE4IManp76sBhBCrLvW4pmmaZn1WmbN0oWmYUsrJl3pc0zRNsz6n33tH0zTNmeiir2ma5kR00dc0TXMiuuhrmqY5EV30NU3TnIjN77IphCgAjnfiJYOBwhafZb/0+7Nv+v3Zt858f7FSyp8drmHzRb+zCSF2XGiTIkeh35990+/PvtnC+9PdO5qmaU5EF31N0zQnoov+zy1QHcDK9Puzb/r92Tfl70/36WuapjkR3dLXNE1zIrroa5qmORGnLfpCiFlCiElCiD+053Fbd6n8QoiApsdnCSGeVpGvo1r79XHU9yeESGz+GnZ2Nktow/ffvM7OZglN2S96VojK+uKURf+cw9lXAyXNe/u39nFb14r8s4HAprMNsLdvrNZ+fZruj+vMbJbQyvd37zlnU9jVe2zF998kILPp8UwhhPWPk7KwS50Torq+OGXRxzicPbPpz82Hs7flcVt3yfxSygVNR1KCURTt7SCbFr8+TYUw8/z77cQl31/TD+mdQog4KeUzUkp7e58tff12AJ81Ffs4KeWuzgzXCZTWF2ct+h0+vN3GtSp/U2EsssOi0Zr3F2eH76tZS++vZ9NHkRBivhDi/Ofbuku+PyllCTAf+AzjfToapfXFWYt+hw9vt3GtzT9LSnmvtcNYwSXfnxBikp0fw9mar9/RpuK4E7Cr7jla/vrNAlZLKXue83dHorS+OGvRd/TD21vML4SY1Xxspb2NWdDy+ytqGiSbhdHnbW/dc635/9ksAKOI2JOW3t+5XTpPYt8NsAtRWl+csug7+uHtLb2/pvufFkLsFELsVJe0fVrx9dvVdF8gP/9V2ua18v9nwDmPK1/l2RYtvT9ggRBiXtPjs+3t/cHZ306Szv0txVbqi16Rq2ma5kScsqWvaZrmrHTR1zRNcyK66GuapjkRXfQ1TdOciC76mqZpTkQXfU3TNCeii76maZoT+f8t0VJpfIua6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_offset1=torch.tensor([0], requires_grad=False, device=device)\n",
    "sample_offset2=torch.tensor([0], requires_grad=False, device=device)\n",
    "sample_data1 = generate_data(offset=sample_offset1, alpha=2, beta=4)\n",
    "sample_data2 = generate_data(offset=sample_offset2, alpha=4, beta=2)\n",
    "\n",
    "print(sample_data1.min(), sample_data1.max())\n",
    "print(sample_data2.min(), sample_data2.max())\n",
    "\n",
    "plt.figure()\n",
    "sns.kdeplot(np.array(sample_data1.cpu()), bw_adjust=0.5, color=palette[0])\n",
    "sns.kdeplot(np.array(sample_data2.cpu()), bw_adjust=0.5, color=palette[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-2.9996) tensor(-2.0933)\n",
      "tensor(0.0757) tensor(0.9978)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f89194a2950>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_offset1=torch.tensor([-3], requires_grad=False, device=device)\n",
    "sample_offset2=torch.tensor([0], requires_grad=False, device=device)\n",
    "sample_data1 = generate_data(offset=sample_offset1, alpha=2, beta=4)\n",
    "sample_data2 = generate_data(offset=sample_offset2, alpha=4, beta=2)\n",
    "\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "print(sample_data1.min(), sample_data1.max())\n",
    "print(sample_data2.min(), sample_data2.max())\n",
    "\n",
    "sns.kdeplot(np.array(sample_data1.cpu()), bw_adjust=0.5, color=palette[0])\n",
    "sns.kdeplot(np.array(sample_data2.cpu()), bw_adjust=0.5, color=palette[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Sequential):\n",
    "    def __init__(self, in_sizes=2, num_hidden=128, depth=2, spectral_norm=False):\n",
    "        in_sizes = [in_sizes] + [num_hidden] * (depth - 1)        \n",
    "        out_sizes = [num_hidden] * (depth - 1) + [1]\n",
    "        if spectral_norm:\n",
    "            layers = sum(\n",
    "                [[nn.utils.spectral_norm(nn.Linear(in_size, out_size)), nn.LeakyReLU()] for in_size, out_size in zip(in_sizes, out_sizes)], \n",
    "                [])\n",
    "        else:\n",
    "            layers = sum(\n",
    "                [[nn.Linear(in_size, out_size), nn.LeakyReLU()] for in_size, out_size in zip(in_sizes, out_sizes)], \n",
    "                [])\n",
    "        layers = layers[:-1]\n",
    "        super(Discriminator, self).__init__(*layers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:     0 err_D_t: 0.761 err_D_s: 0.707 err_G: 0.747 offset: -2.999\n",
      "step:    20 err_D_t: 0.044 err_D_s: 0.052 err_G: 3.151 offset: -1.767\n",
      "step:    40 err_D_t: 1.620 err_D_s: 0.674 err_G: 1.036 offset: 1.239\n",
      "step:    60 err_D_t: 0.660 err_D_s: 0.384 err_G: 1.278 offset: 2.154\n",
      "step:    80 err_D_t: 0.348 err_D_s: 0.821 err_G: 0.697 offset: 0.497\n",
      "step:   100 err_D_t: 0.497 err_D_s: 0.641 err_G: 0.837 offset: -0.210\n",
      "step:   120 err_D_t: 0.820 err_D_s: 0.541 err_G: 0.937 offset: 0.613\n",
      "step:   140 err_D_t: 0.629 err_D_s: 0.772 err_G: 0.625 offset: 0.330\n",
      "step:   160 err_D_t: 0.726 err_D_s: 0.667 err_G: 0.728 offset: 0.286\n",
      "step:   180 err_D_t: 0.688 err_D_s: 0.708 err_G: 0.688 offset: 0.360\n",
      "step:   200 err_D_t: 0.699 err_D_s: 0.695 err_G: 0.697 offset: 0.327\n",
      "step:   220 err_D_t: 0.698 err_D_s: 0.694 err_G: 0.700 offset: 0.330\n",
      "step:   240 err_D_t: 0.696 err_D_s: 0.697 err_G: 0.697 offset: 0.336\n",
      "step:   260 err_D_t: 0.697 err_D_s: 0.697 err_G: 0.697 offset: 0.336\n",
      "step:   280 err_D_t: 0.694 err_D_s: 0.696 err_G: 0.699 offset: 0.339\n",
      "step:   300 err_D_t: 0.699 err_D_s: 0.693 err_G: 0.702 offset: 0.339\n",
      "step:   320 err_D_t: 0.694 err_D_s: 0.692 err_G: 0.703 offset: 0.341\n",
      "step:   340 err_D_t: 0.696 err_D_s: 0.690 err_G: 0.706 offset: 0.346\n",
      "step:   360 err_D_t: 0.699 err_D_s: 0.691 err_G: 0.704 offset: 0.341\n",
      "step:   380 err_D_t: 0.697 err_D_s: 0.689 err_G: 0.707 offset: 0.350\n",
      "step:   400 err_D_t: 0.697 err_D_s: 0.684 err_G: 0.714 offset: 0.350\n",
      "step:   420 err_D_t: 0.700 err_D_s: 0.684 err_G: 0.714 offset: 0.353\n",
      "step:   440 err_D_t: 0.693 err_D_s: 0.685 err_G: 0.713 offset: 0.352\n",
      "step:   460 err_D_t: 0.695 err_D_s: 0.683 err_G: 0.716 offset: 0.355\n",
      "step:   480 err_D_t: 0.693 err_D_s: 0.685 err_G: 0.713 offset: 0.349\n",
      "step:   500 err_D_t: 0.694 err_D_s: 0.682 err_G: 0.715 offset: 0.353\n",
      "step:   520 err_D_t: 0.692 err_D_s: 0.683 err_G: 0.713 offset: 0.345\n",
      "step:   540 err_D_t: 0.691 err_D_s: 0.684 err_G: 0.712 offset: 0.345\n",
      "step:   560 err_D_t: 0.689 err_D_s: 0.682 err_G: 0.715 offset: 0.343\n",
      "step:   580 err_D_t: 0.690 err_D_s: 0.682 err_G: 0.713 offset: 0.335\n",
      "step:   600 err_D_t: 0.686 err_D_s: 0.683 err_G: 0.714 offset: 0.341\n",
      "step:   620 err_D_t: 0.688 err_D_s: 0.683 err_G: 0.712 offset: 0.329\n",
      "step:   640 err_D_t: 0.687 err_D_s: 0.685 err_G: 0.709 offset: 0.339\n",
      "step:   660 err_D_t: 0.685 err_D_s: 0.685 err_G: 0.708 offset: 0.329\n",
      "step:   680 err_D_t: 0.686 err_D_s: 0.686 err_G: 0.709 offset: 0.333\n",
      "step:   700 err_D_t: 0.686 err_D_s: 0.686 err_G: 0.708 offset: 0.331\n",
      "step:   720 err_D_t: 0.685 err_D_s: 0.684 err_G: 0.711 offset: 0.329\n",
      "step:   740 err_D_t: 0.687 err_D_s: 0.683 err_G: 0.711 offset: 0.331\n",
      "step:   760 err_D_t: 0.684 err_D_s: 0.686 err_G: 0.708 offset: 0.330\n",
      "step:   780 err_D_t: 0.685 err_D_s: 0.684 err_G: 0.710 offset: 0.327\n",
      "step:   800 err_D_t: 0.684 err_D_s: 0.686 err_G: 0.708 offset: 0.327\n",
      "step:   820 err_D_t: 0.689 err_D_s: 0.683 err_G: 0.711 offset: 0.330\n",
      "step:   840 err_D_t: 0.684 err_D_s: 0.686 err_G: 0.708 offset: 0.327\n",
      "step:   860 err_D_t: 0.684 err_D_s: 0.683 err_G: 0.711 offset: 0.322\n",
      "step:   880 err_D_t: 0.686 err_D_s: 0.686 err_G: 0.708 offset: 0.331\n",
      "step:   900 err_D_t: 0.686 err_D_s: 0.682 err_G: 0.712 offset: 0.323\n",
      "step:   920 err_D_t: 0.683 err_D_s: 0.686 err_G: 0.708 offset: 0.330\n",
      "step:   940 err_D_t: 0.685 err_D_s: 0.683 err_G: 0.711 offset: 0.318\n",
      "step:   960 err_D_t: 0.684 err_D_s: 0.685 err_G: 0.711 offset: 0.331\n",
      "step:   980 err_D_t: 0.684 err_D_s: 0.683 err_G: 0.711 offset: 0.326\n",
      "step:  1000 err_D_t: 0.682 err_D_s: 0.685 err_G: 0.710 offset: 0.326\n",
      "step:  1020 err_D_t: 0.689 err_D_s: 0.683 err_G: 0.713 offset: 0.329\n",
      "step:  1040 err_D_t: 0.682 err_D_s: 0.686 err_G: 0.710 offset: 0.325\n",
      "step:  1060 err_D_t: 0.683 err_D_s: 0.685 err_G: 0.710 offset: 0.330\n",
      "step:  1080 err_D_t: 0.682 err_D_s: 0.685 err_G: 0.710 offset: 0.323\n",
      "step:  1100 err_D_t: 0.684 err_D_s: 0.687 err_G: 0.708 offset: 0.328\n",
      "step:  1120 err_D_t: 0.685 err_D_s: 0.687 err_G: 0.708 offset: 0.326\n",
      "step:  1140 err_D_t: 0.684 err_D_s: 0.684 err_G: 0.713 offset: 0.328\n",
      "step:  1160 err_D_t: 0.685 err_D_s: 0.684 err_G: 0.711 offset: 0.326\n",
      "step:  1180 err_D_t: 0.685 err_D_s: 0.683 err_G: 0.713 offset: 0.328\n",
      "step:  1200 err_D_t: 0.684 err_D_s: 0.685 err_G: 0.711 offset: 0.327\n",
      "step:  1220 err_D_t: 0.682 err_D_s: 0.685 err_G: 0.709 offset: 0.321\n",
      "step:  1240 err_D_t: 0.684 err_D_s: 0.684 err_G: 0.712 offset: 0.328\n",
      "step:  1260 err_D_t: 0.685 err_D_s: 0.686 err_G: 0.710 offset: 0.328\n",
      "step:  1280 err_D_t: 0.685 err_D_s: 0.684 err_G: 0.712 offset: 0.322\n",
      "step:  1300 err_D_t: 0.684 err_D_s: 0.684 err_G: 0.712 offset: 0.329\n",
      "step:  1320 err_D_t: 0.684 err_D_s: 0.684 err_G: 0.712 offset: 0.323\n",
      "step:  1340 err_D_t: 0.684 err_D_s: 0.686 err_G: 0.710 offset: 0.331\n",
      "step:  1360 err_D_t: 0.683 err_D_s: 0.685 err_G: 0.711 offset: 0.321\n",
      "step:  1380 err_D_t: 0.683 err_D_s: 0.683 err_G: 0.715 offset: 0.331\n",
      "step:  1400 err_D_t: 0.684 err_D_s: 0.683 err_G: 0.714 offset: 0.321\n",
      "step:  1420 err_D_t: 0.681 err_D_s: 0.686 err_G: 0.712 offset: 0.327\n",
      "step:  1440 err_D_t: 0.684 err_D_s: 0.684 err_G: 0.713 offset: 0.325\n",
      "step:  1460 err_D_t: 0.681 err_D_s: 0.681 err_G: 0.717 offset: 0.323\n",
      "step:  1480 err_D_t: 0.683 err_D_s: 0.682 err_G: 0.715 offset: 0.328\n",
      "step:  1500 err_D_t: 0.684 err_D_s: 0.687 err_G: 0.709 offset: 0.322\n",
      "step:  1520 err_D_t: 0.682 err_D_s: 0.686 err_G: 0.710 offset: 0.325\n",
      "step:  1540 err_D_t: 0.680 err_D_s: 0.683 err_G: 0.714 offset: 0.323\n",
      "step:  1560 err_D_t: 0.683 err_D_s: 0.682 err_G: 0.717 offset: 0.327\n",
      "step:  1580 err_D_t: 0.681 err_D_s: 0.682 err_G: 0.716 offset: 0.322\n",
      "step:  1600 err_D_t: 0.684 err_D_s: 0.681 err_G: 0.719 offset: 0.331\n",
      "step:  1620 err_D_t: 0.684 err_D_s: 0.685 err_G: 0.711 offset: 0.324\n",
      "step:  1640 err_D_t: 0.685 err_D_s: 0.683 err_G: 0.716 offset: 0.326\n",
      "step:  1660 err_D_t: 0.683 err_D_s: 0.683 err_G: 0.716 offset: 0.327\n",
      "step:  1680 err_D_t: 0.682 err_D_s: 0.684 err_G: 0.715 offset: 0.326\n",
      "step:  1700 err_D_t: 0.683 err_D_s: 0.682 err_G: 0.716 offset: 0.320\n",
      "step:  1720 err_D_t: 0.681 err_D_s: 0.684 err_G: 0.715 offset: 0.324\n",
      "step:  1740 err_D_t: 0.683 err_D_s: 0.683 err_G: 0.715 offset: 0.328\n",
      "step:  1760 err_D_t: 0.681 err_D_s: 0.685 err_G: 0.712 offset: 0.319\n",
      "step:  1780 err_D_t: 0.683 err_D_s: 0.683 err_G: 0.715 offset: 0.328\n",
      "step:  1800 err_D_t: 0.683 err_D_s: 0.685 err_G: 0.713 offset: 0.320\n",
      "step:  1820 err_D_t: 0.682 err_D_s: 0.682 err_G: 0.718 offset: 0.326\n",
      "step:  1840 err_D_t: 0.684 err_D_s: 0.682 err_G: 0.717 offset: 0.323\n",
      "step:  1860 err_D_t: 0.683 err_D_s: 0.685 err_G: 0.713 offset: 0.323\n",
      "step:  1880 err_D_t: 0.681 err_D_s: 0.686 err_G: 0.712 offset: 0.323\n",
      "step:  1900 err_D_t: 0.683 err_D_s: 0.683 err_G: 0.716 offset: 0.326\n",
      "step:  1920 err_D_t: 0.682 err_D_s: 0.681 err_G: 0.719 offset: 0.322\n",
      "step:  1940 err_D_t: 0.684 err_D_s: 0.684 err_G: 0.715 offset: 0.326\n",
      "step:  1960 err_D_t: 0.681 err_D_s: 0.683 err_G: 0.716 offset: 0.326\n",
      "step:  1980 err_D_t: 0.683 err_D_s: 0.682 err_G: 0.717 offset: 0.320\n",
      "step:  2000 err_D_t: 0.684 err_D_s: 0.681 err_G: 0.717 offset: 0.325\n"
     ]
    }
   ],
   "source": [
    "num_samples=5000\n",
    "dim=1\n",
    "num_hidden=32\n",
    "depth=2\n",
    "\n",
    "target_offset = torch.tensor([0], requires_grad=False, device=device)\n",
    "offset = torch.tensor([-3.], requires_grad=True, device=device)\n",
    "\n",
    "torch.random.manual_seed(1)\n",
    "d_net = Discriminator(in_sizes=dim, num_hidden=num_hidden, depth=depth, spectral_norm=False)\n",
    "d_net.to(device)\n",
    "\n",
    "d_opt = torch.optim.SGD(d_net.parameters(), lr=0.01, momentum=0.9)\n",
    "o_opt = torch.optim.SGD([offset], lr=0.01, momentum=0.9)\n",
    "\n",
    "num_steps = 2001\n",
    "\n",
    "for step in range(num_steps):\n",
    "    label0 = torch.full((num_samples,), 0., device=device)\n",
    "    label1 = torch.full((num_samples,), 1., device=device)\n",
    "    target_data = generate_data(num_samples=num_samples, offset=target_offset,\n",
    "                                alpha=4, beta=2).view(-1, 1)\n",
    "    source_data = generate_data(num_samples=num_samples, offset=offset,\n",
    "                                alpha=2, beta=4).view(-1, 1)\n",
    "    \n",
    "    # D\n",
    "    d_net.zero_grad()\n",
    "    d_source = d_net(source_data.detach()).view(-1)\n",
    "    d_target = d_net(target_data).view(-1)\n",
    "    \n",
    "    loss_d_source = F.binary_cross_entropy_with_logits(d_source, label0)\n",
    "    loss_d_target = F.binary_cross_entropy_with_logits(d_target, label1)\n",
    "    \n",
    "    (loss_d_source + loss_d_target).backward()\n",
    "    d_opt.step()\n",
    "\n",
    "    # G\n",
    "    if offset.grad is not None:\n",
    "        offset.grad.detach_()\n",
    "        offset.grad.zero_()\n",
    "    d_source = d_net(source_data).view(-1)\n",
    "    loss_g_source = F.binary_cross_entropy_with_logits(d_source, label1)\n",
    "    loss_g_source.backward()\n",
    "    o_opt.step()\n",
    "    \n",
    "    if step % 20 == 0:\n",
    "        print(\"step: %5d err_D_t: %.3f err_D_s: %.3f err_G: %.3f offset: %.3f\" %\n",
    "              (step, loss_d_target.item(), loss_d_source.item(), loss_g_source.item(), offset.item()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada_data1 = generate_data(offset=offset, alpha=2, beta=4)\n",
    "ada_data2 = generate_data(offset=target_offset, alpha=4, beta=2)\n",
    "\n",
    "plt.figure()\n",
    "sns.kdeplot(np.array(ada_data1.detach().cpu()), bw_adjust=0.5, color=palette[0])\n",
    "sns.kdeplot(np.array(ada_data2.detach().cpu()), bw_adjust=0.5, color=palette[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.3260, grad_fn=<MinBackward1>) tensor(1.2464, grad_fn=<MaxBackward1>)\n",
      "\n",
      "tensor(0.0962) tensor(0.9970)\n"
     ]
    }
   ],
   "source": [
    "print(ada_data1.min(), ada_data1.max())\n",
    "print()\n",
    "print(ada_data2.min(), ada_data2.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:     0 err_D_t: 0.761 err_D_s: 0.707 dis_s_t: 0.000 dis_t_s: 0.000 offset: -3.000\n",
      "step:    20 err_D_t: 0.743 err_D_s: 1.061 dis_s_t: 0.000 dis_t_s: 0.017 offset: 2.564\n",
      "step:    40 err_D_t: 0.299 err_D_s: 0.429 dis_s_t: 1.783 dis_t_s: 0.426 offset: -0.824\n",
      "step:    60 err_D_t: 0.876 err_D_s: 0.231 dis_s_t: 0.156 dis_t_s: 0.303 offset: 1.054\n",
      "step:    80 err_D_t: 0.572 err_D_s: 0.657 dis_s_t: 0.002 dis_t_s: 0.000 offset: -0.093\n",
      "step:   100 err_D_t: 0.540 err_D_s: 0.485 dis_s_t: 0.004 dis_t_s: 0.000 offset: 0.000\n",
      "step:   120 err_D_t: 0.559 err_D_s: 0.535 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.095\n",
      "step:   140 err_D_t: 0.538 err_D_s: 0.543 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.106\n",
      "step:   160 err_D_t: 0.536 err_D_s: 0.532 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.107\n",
      "step:   180 err_D_t: 0.535 err_D_s: 0.539 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.107\n",
      "step:   200 err_D_t: 0.544 err_D_s: 0.531 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.106\n",
      "step:   220 err_D_t: 0.534 err_D_s: 0.538 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.106\n",
      "step:   240 err_D_t: 0.531 err_D_s: 0.542 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.105\n",
      "step:   260 err_D_t: 0.525 err_D_s: 0.530 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:   280 err_D_t: 0.520 err_D_s: 0.528 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:   300 err_D_t: 0.525 err_D_s: 0.535 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:   320 err_D_t: 0.512 err_D_s: 0.518 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:   340 err_D_t: 0.529 err_D_s: 0.536 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:   360 err_D_t: 0.540 err_D_s: 0.512 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:   380 err_D_t: 0.529 err_D_s: 0.528 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:   400 err_D_t: 0.525 err_D_s: 0.531 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:   420 err_D_t: 0.535 err_D_s: 0.532 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:   440 err_D_t: 0.510 err_D_s: 0.521 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:   460 err_D_t: 0.522 err_D_s: 0.535 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   480 err_D_t: 0.524 err_D_s: 0.524 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   500 err_D_t: 0.526 err_D_s: 0.510 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
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      "step:   540 err_D_t: 0.525 err_D_s: 0.512 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   560 err_D_t: 0.526 err_D_s: 0.527 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
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      "step:   600 err_D_t: 0.518 err_D_s: 0.544 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:   620 err_D_t: 0.526 err_D_s: 0.528 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:   640 err_D_t: 0.523 err_D_s: 0.507 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:   660 err_D_t: 0.513 err_D_s: 0.513 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   680 err_D_t: 0.525 err_D_s: 0.531 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   700 err_D_t: 0.521 err_D_s: 0.528 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   720 err_D_t: 0.527 err_D_s: 0.528 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:   740 err_D_t: 0.526 err_D_s: 0.518 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   760 err_D_t: 0.531 err_D_s: 0.531 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:   780 err_D_t: 0.516 err_D_s: 0.514 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.100\n",
      "step:   800 err_D_t: 0.524 err_D_s: 0.521 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:   820 err_D_t: 0.544 err_D_s: 0.520 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   840 err_D_t: 0.524 err_D_s: 0.536 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:   860 err_D_t: 0.531 err_D_s: 0.524 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:   880 err_D_t: 0.530 err_D_s: 0.517 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.100\n",
      "step:   900 err_D_t: 0.530 err_D_s: 0.518 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.100\n",
      "step:   920 err_D_t: 0.515 err_D_s: 0.529 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.100\n",
      "step:   940 err_D_t: 0.527 err_D_s: 0.529 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.099\n",
      "step:   960 err_D_t: 0.525 err_D_s: 0.524 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.099\n",
      "step:   980 err_D_t: 0.517 err_D_s: 0.509 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.100\n",
      "step:  1000 err_D_t: 0.520 err_D_s: 0.511 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.100\n",
      "step:  1020 err_D_t: 0.541 err_D_s: 0.520 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.099\n",
      "step:  1040 err_D_t: 0.515 err_D_s: 0.532 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.098\n",
      "step:  1060 err_D_t: 0.504 err_D_s: 0.520 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.099\n",
      "step:  1080 err_D_t: 0.511 err_D_s: 0.516 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.099\n",
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      "step:  1120 err_D_t: 0.525 err_D_s: 0.514 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.098\n",
      "step:  1140 err_D_t: 0.514 err_D_s: 0.547 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.099\n",
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      "step:  1220 err_D_t: 0.517 err_D_s: 0.512 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:  1240 err_D_t: 0.524 err_D_s: 0.517 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
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      "step:  1320 err_D_t: 0.534 err_D_s: 0.521 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:  1340 err_D_t: 0.526 err_D_s: 0.519 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
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      "step:  1380 err_D_t: 0.518 err_D_s: 0.539 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:  1400 err_D_t: 0.532 err_D_s: 0.525 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
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      "step:  1440 err_D_t: 0.529 err_D_s: 0.534 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:  1460 err_D_t: 0.519 err_D_s: 0.533 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
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      "step:  1500 err_D_t: 0.534 err_D_s: 0.514 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:  1520 err_D_t: 0.515 err_D_s: 0.520 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:  1540 err_D_t: 0.513 err_D_s: 0.539 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:  1560 err_D_t: 0.522 err_D_s: 0.540 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:  1580 err_D_t: 0.522 err_D_s: 0.519 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:  1600 err_D_t: 0.523 err_D_s: 0.550 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:  1620 err_D_t: 0.528 err_D_s: 0.531 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:  1640 err_D_t: 0.530 err_D_s: 0.529 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:  1660 err_D_t: 0.529 err_D_s: 0.533 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:  1680 err_D_t: 0.515 err_D_s: 0.536 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:  1700 err_D_t: 0.524 err_D_s: 0.530 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:  1720 err_D_t: 0.530 err_D_s: 0.538 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n",
      "step:  1740 err_D_t: 0.529 err_D_s: 0.529 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:  1760 err_D_t: 0.524 err_D_s: 0.533 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:  1780 err_D_t: 0.521 err_D_s: 0.532 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:  1800 err_D_t: 0.526 err_D_s: 0.528 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.104\n",
      "step:  1820 err_D_t: 0.520 err_D_s: 0.532 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:  1840 err_D_t: 0.542 err_D_s: 0.527 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:  1860 err_D_t: 0.531 err_D_s: 0.528 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:  1880 err_D_t: 0.522 err_D_s: 0.521 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.103\n",
      "step:  1900 err_D_t: 0.527 err_D_s: 0.525 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:  1920 err_D_t: 0.521 err_D_s: 0.526 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:  1940 err_D_t: 0.538 err_D_s: 0.527 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:  1960 err_D_t: 0.515 err_D_s: 0.520 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:  1980 err_D_t: 0.528 err_D_s: 0.526 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.101\n",
      "step:  2000 err_D_t: 0.528 err_D_s: 0.508 dis_s_t: 0.000 dis_t_s: 0.000 offset: 0.100\n"
     ]
    }
   ],
   "source": [
    "num_samples=5000\n",
    "dim=1\n",
    "num_hidden=32\n",
    "depth=2\n",
    "\n",
    "target_offset = torch.tensor([0], requires_grad=False, device=device)\n",
    "offset = torch.tensor([-3.], requires_grad=True, device=device)\n",
    "\n",
    "torch.random.manual_seed(1)\n",
    "d_net = Discriminator(in_sizes=dim, num_hidden=num_hidden, depth=depth, spectral_norm=False)\n",
    "d_net.to(device)\n",
    "\n",
    "d_opt = torch.optim.SGD(d_net.parameters(), lr=0.01, momentum=0.9)\n",
    "o_opt = torch.optim.SGD([offset], lr=0.01, momentum=0.9)\n",
    "\n",
    "num_steps = 2001\n",
    "\n",
    "for step in range(num_steps):\n",
    "    label0 = torch.full((num_samples,), 0., device=device)\n",
    "    label1 = torch.full((num_samples,), 1., device=device)\n",
    "    target_data = generate_data(num_samples=num_samples, offset=target_offset,\n",
    "                                alpha=4, beta=2).view(-1, 1)\n",
    "    source_data = generate_data(num_samples=num_samples, offset=offset,\n",
    "                                alpha=2, beta=4).view(-1, 1)\n",
    "\n",
    "    # D\n",
    "    d_net.zero_grad()\n",
    "    d_source = d_net(source_data.detach()).view(-1)\n",
    "    d_target = d_net(target_data).view(-1)\n",
    "    \n",
    "    loss_d_source = F.binary_cross_entropy_with_logits(d_source, label0)\n",
    "    loss_d_target = F.binary_cross_entropy_with_logits(d_target, label1)\n",
    "    \n",
    "    (loss_d_source + loss_d_target).backward()\n",
    "    d_opt.step()\n",
    "\n",
    "    # G\n",
    "    if offset.grad is not None:\n",
    "        offset.grad.detach_()\n",
    "        offset.grad.zero_()\n",
    "    d_source = d_net(source_data).view(-1)\n",
    "    d_target = d_net(target_data).view(-1)\n",
    "    d_dis = (d_source[:, None] - d_target[None, :]) ** 2\n",
    "    dis_s_t = torch.mean(torch.min(d_dis, dim=1)[0])\n",
    "    dis_t_s = torch.mean(torch.min(d_dis, dim=0)[0])\n",
    "    (dis_s_t + dis_t_s).backward()\n",
    "    o_opt.step()\n",
    "    \n",
    "    if step % 20 == 0:\n",
    "        print(\"step: %5d err_D_t: %.3f err_D_s: %.3f dis_s_t: %.3f dis_t_s: %.3f offset: %.3f\" %\n",
    "              (step, loss_d_target.item(), loss_d_source.item(), dis_s_t.item(), dis_t_s.item(),\n",
    "               offset.item()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "asa_data1 = generate_data(offset=offset, alpha=2, beta=4)\n",
    "asa_data2 = generate_data(offset=target_offset, alpha=4, beta=2)\n",
    "\n",
    "plt.figure()\n",
    "sns.kdeplot(np.array(asa_data1.detach().cpu()), bw_adjust=0.5, color=palette[0])\n",
    "sns.kdeplot(np.array(asa_data2.detach().cpu()), bw_adjust=0.5, color=palette[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1067, grad_fn=<MinBackward1>) tensor(1.0092, grad_fn=<MaxBackward1>)\n",
      "\n",
      "tensor(0.0719) tensor(0.9942)\n"
     ]
    }
   ],
   "source": [
    "print(asa_data1.min(), asa_data1.max())\n",
    "print()\n",
    "print(asa_data2.min(), asa_data2.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def alignment_histplot(src_tensor, trg_tensor, title=None, legend=True, ylabel=True):\n",
    "\n",
    "    src_array = src_tensor.numpy()\n",
    "    trg_array = trg_tensor.numpy()\n",
    "\n",
    "    mx = max(src_array.max(), trg_array.max())\n",
    "    mn = min(src_array.min(), trg_array.min())\n",
    "    d = max(mx - mn, 0.01)\n",
    "    mn = mn - d * 0.12\n",
    "    mx = mx + d * 0.12\n",
    "    \n",
    "    ax = sns.kdeplot(x=src_array, color=palette[0] + (0.75,), linewidth=6, fill=False, label='source')\n",
    "    ax = sns.kdeplot(x=trg_array, color=palette[1] + (0.75,), linewidth=6, fill=False, label='target')    \n",
    "    \n",
    "    l1 = ax.lines[0]\n",
    "    l2 = ax.lines[1]\n",
    "\n",
    "    # Get the xy data from the lines so that we can shade\n",
    "    x1 = l1.get_xydata()[:,0]\n",
    "    y1 = l1.get_xydata()[:,1]\n",
    "    x2 = l2.get_xydata()[:,0]\n",
    "    y2 = l2.get_xydata()[:,1]\n",
    "    ax.fill_between(x1,y1, color=palette[0], alpha=0.25)\n",
    "    ax.fill_between(x2,y2, color=palette[1], alpha=0.25)\n",
    "    \n",
    "    source_patch = matplotlib.patches.Patch(label='$p(x)$', edgecolor=palette[0] + (0.75,), \n",
    "                                            facecolor=palette[0] + (0.25,), linewidth=6)\n",
    "    target_patch = matplotlib.patches.Patch(label='$q^\\\\theta(x)$', edgecolor=palette[1] + (0.75,), \n",
    "                                            facecolor=palette[1] + (0.25,), linewidth=6)    \n",
    "\n",
    "    if ylabel:\n",
    "        plt.ylabel('Density', fontsize=55, labelpad=10)\n",
    "    else:\n",
    "        plt.ylabel('', fontsize=75, labelpad=20)\n",
    "    plt.xlabel('$x$', fontsize=55, labelpad=10)    \n",
    "    plt.margins(y=0.45)    \n",
    "    if legend:        \n",
    "        plt.legend(handles=[source_patch, target_patch], fontsize=55, loc='upper center', bbox_to_anchor=(0.5, 1.04), ncol=1, frameon=True, handlelength=1.25, columnspacing=1.0)\n",
    "    \n",
    "    plt.gca().set_xlim(mn, mx)\n",
    "    plt.grid(linestyle='--', dashes=(12, 12))    \n",
    "    ax = plt.gca()\n",
    "\n",
    "    tick_labels = ax.xaxis.get_majorticklabels() + ax.yaxis.get_majorticklabels()\n",
    "\n",
    "    for label in tick_labels:\n",
    "        label.set_fontsize(55)\n",
    "\n",
    "    ax.tick_params(axis='both', which='major', pad=15)\n",
    "\n",
    "    if title is not None:\n",
    "        plt.title(title, fontsize=42, y=1.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def w2_sq_dist(v1, v2):\n",
    "    return torch.mean(torch.square(torch.sort(v1)[0] - torch.sort(v2)[0])).item()\n",
    "\n",
    "def supp_sq_dist(v1, v2):\n",
    "    dist_mat = torch.square(v1[:, None] - v2[None, :])\n",
    "    dist_1_2 = torch.mean(torch.min(dist_mat, dim=1)[0])\n",
    "    dist_2_1 = torch.mean(torch.min(dist_mat, dim=0)[0])\n",
    "    \n",
    "    return (dist_1_2 + dist_2_1).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init\n",
      "W2_sq: 11.1147270 supp_sq: 15.2028332\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "da\n",
      "W2_sq: 0.0024852 supp_sq: 0.0007840\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sa\n",
      "W2_sq: 0.0575050 supp_sq: 0.0000028\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = [\n",
    "    ('init', sample_data2.detach(), sample_data1.detach()),\n",
    "    ('da', ada_data2.detach(), ada_data1.detach()),\n",
    "    ('sa', asa_data2.detach(), asa_data1.detach()),\n",
    "]\n",
    "\n",
    "for i, (name, t1, t2) in enumerate(results):\n",
    "    print(name)\n",
    "    print(f'W2_sq: {w2_sq_dist(t1, t2):.7f} supp_sq: {supp_sq_dist(t1, t2):.7f}')\n",
    "    plt.figure(figsize=(14, 10.0))\n",
    "    alignment_histplot(t1, t2, title=None, legend=i == 0, ylabel=i == 0)\n",
    "    plt.savefig(f'./figures/betaplot_{name}.pdf', format='pdf', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
